Merge branch 4.x

2026-07-29 23:33:05 +04:00 · 2021-04-09 10:30:38 +00:00
parent 263de1f86e bfb10d74eb
commit fc628014bb
1114 changed files with 64039 additions and 14611 deletions
@@ -20,10 +20,10 @@ scale invariant.

 ![image](images/sift_scale_invariant.jpg)

-So, in 2004, **D.Lowe**, University of British Columbia, came up with a new algorithm, Scale
+In 2004, **D.Lowe**, University of British Columbia, came up with a new algorithm, Scale
 Invariant Feature Transform (SIFT) in his paper, **Distinctive Image Features from Scale-Invariant
 Keypoints**, which extract keypoints and compute its descriptors. *(This paper is easy to understand
-and considered to be best material available on SIFT. So this explanation is just a short summary of
+and considered to be best material available on SIFT. This explanation is just a short summary of
 this paper)*.

 There are mainly four steps involved in SIFT algorithm. We will see them one-by-one.
@@ -102,16 +102,17 @@ reasons. In that case, ratio of closest-distance to second-closest distance is t
 greater than 0.8, they are rejected. It eliminates around 90% of false matches while discards only
 5% correct matches, as per the paper.

-So this is a summary of SIFT algorithm. For more details and understanding, reading the original
-paper is highly recommended. Remember one thing, this algorithm is patented. So this algorithm is
-included in [the opencv contrib repo](https://github.com/opencv/opencv_contrib)
+This is a summary of SIFT algorithm. For more details and understanding, reading the original
+paper is highly recommended.

 SIFT in OpenCV
 --------------

-So now let's see SIFT functionalities available in OpenCV. Let's start with keypoint detection and
-draw them. First we have to construct a SIFT object. We can pass different parameters to it which
-are optional and they are well explained in docs.
+Now let's see SIFT functionalities available in OpenCV. Note that these were previously only
+available in [the opencv contrib repo](https://github.com/opencv/opencv_contrib), but the patent
+expired in the year 2020. So they are now included in the main repo. Let's start with keypoint
+detection and draw them. First we have to construct a SIFT object. We can pass different
+parameters to it which are optional and they are well explained in docs.
@code{.py}
 import numpy as np
 import cv2 as cv
@@ -88,27 +88,27 @@ B = cv.imread('orange.jpg')
 # generate Gaussian pyramid for A
 G = A.copy()
 gpA = [G]
-for i in xrange(6):
+for i in range(6):
    G = cv.pyrDown(G)
    gpA.append(G)

 # generate Gaussian pyramid for B
 G = B.copy()
 gpB = [G]
-for i in xrange(6):
+for i in range(6):
    G = cv.pyrDown(G)
    gpB.append(G)

 # generate Laplacian Pyramid for A
 lpA = [gpA[5]]
-for i in xrange(5,0,-1):
+for i in range(5,0,-1):
    GE = cv.pyrUp(gpA[i])
    L = cv.subtract(gpA[i-1],GE)
    lpA.append(L)

 # generate Laplacian Pyramid for B
 lpB = [gpB[5]]
-for i in xrange(5,0,-1):
+for i in range(5,0,-1):
    GE = cv.pyrUp(gpB[i])
    L = cv.subtract(gpB[i-1],GE)
    lpB.append(L)
@@ -122,7 +122,7 @@ for la,lb in zip(lpA,lpB):

 # now reconstruct
 ls_ = LS[0]
-for i in xrange(1,6):
+for i in range(1,6):
    ls_ = cv.pyrUp(ls_)
    ls_ = cv.add(ls_, LS[i])

@@ -47,7 +47,7 @@ ret,thresh5 = cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)
 titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
 images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

-for i in xrange(6):
+for i in range(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
@@ -98,7 +98,7 @@ titles = ['Original Image', 'Global Thresholding (v = 127)',
            'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
 images = [img, th1, th2, th3]

-for i in xrange(4):
+for i in range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
@@ -153,7 +153,7 @@ titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
          'Original Noisy Image','Histogram',"Otsu's Thresholding",
          'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]

-for i in xrange(3):
+for i in range(3):
    plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
    plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
@@ -196,7 +196,7 @@ bins = np.arange(256)
 fn_min = np.inf
 thresh = -1

-for i in xrange(1,256):
+for i in range(1,256):
    p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
    q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
    if q1 < 1.e-6 or q2 < 1.e-6:
@@ -268,7 +268,7 @@ fft_filters = [np.fft.fft2(x) for x in filters]
 fft_shift = [np.fft.fftshift(y) for y in fft_filters]
 mag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift]

-for i in xrange(6):
+for i in range(6):
    plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray')
    plt.title(filter_name[i]), plt.xticks([]), plt.yticks([])

@@ -108,7 +108,7 @@ from matplotlib import pyplot as plt
 cap = cv.VideoCapture('vtest.avi')

 # create a list of first 5 frames
-img = [cap.read()[1] for i in xrange(5)]
+img = [cap.read()[1] for i in range(5)]

 # convert all to grayscale
 gray = [cv.cvtColor(i, cv.COLOR_BGR2GRAY) for i in img]
@@ -83,4 +83,4 @@ Additional Resources
 2.  [NumPy Quickstart tutorial](https://numpy.org/devdocs/user/quickstart.html)
 3.  [NumPy Reference](https://numpy.org/devdocs/reference/index.html#reference)
 4.  [OpenCV Documentation](http://docs.opencv.org/)
-5.  [OpenCV Forum](http://answers.opencv.org/questions/)
+5.  [OpenCV Forum](https://forum.opencv.org/)
@@ -22,10 +22,10 @@ Installing OpenCV-Python from Pre-built Binaries

 This method serves best when using just for programming and developing OpenCV applications.

-Install package [python-opencv](https://packages.ubuntu.com/trusty/python-opencv) with following command in terminal (as root user).
+Install package [python3-opencv](https://packages.ubuntu.com/focal/python3-opencv) with following command in terminal (as root user).

 ```
-$ sudo apt-get install python-opencv
+$ sudo apt-get install python3-opencv
 ```

 Open Python IDLE (or IPython) and type following codes in Python terminal.